SS2 (for AG Hefendehl): Integration
Desirée Brösamle
Dec 2021
Last updated: 2023-08-07
Checks: 7 0
Knit directory: scSeq_Hefendehl/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20220131) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version e103c55. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .RData
Ignored: .Rproj.user/
Ignored: analysis/figure/
Ignored: data/20230203/
Ignored: data/Alignments/
Ignored: data/Cluster.markers_02-02-2023.xlsx
Ignored: data/DAM_genelists.RData
Ignored: data/DEG_control_vs_stroke.xlsx
Ignored: data/ReloadAllData_Hefendehl_Stroke_Dec'21.RData
Ignored: data/Sample_Tables/
Ignored: data/Stroke-SS2_SCT-harmony_Hefendehl.html
Ignored: data/counts.csv
Ignored: data/genecounts.csv
Ignored: data/microglia-SCT_02-02-23.rds
Ignored: data/microglia_protein.rds
Ignored: data/samples.integrated.RData
Ignored: data/seu-SCT-harmony_02-02-23.rds
Ignored: data/seu_filtered_nonorm_02-02-23.rds
Ignored: data/tx2genes.csv
Ignored: output/CrossFCplot.pdf
Ignored: output/CrossFCplot.svg
Ignored: output/integrated_analysis.RData
Ignored: output/integrated_analysis.RDataTmp
Untracked files:
Untracked: shinySecret.R
Unstaged changes:
Modified: geneviewer/Dataset.RData
Modified: geneviewer/rsconnect/shinyapps.io/molgenlab/geneviewer.dcf
Modified: wflowhelper.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/00.1_Original_Analysis_Integrated.Rmd) and HTML
(docs/00.1_Original_Analysis_Integrated.html) files. If
you’ve configured a remote Git repository (see
?wflow_git_remote), click on the hyperlinks in the table
below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e103c55 | Andreas Chiocchetti | 2023-08-07 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
| html | 6d3e973 | Andreas Chiocchetti | 2023-07-14 | Build site. |
| Rmd | d5c8125 | Andreas Chiocchetti | 2023-07-14 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
| html | d5c8125 | Andreas Chiocchetti | 2023-07-14 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
| html | 982b9f6 | Andreas Chiocchetti | 2023-02-20 | Build site. |
| Rmd | 438f7ad | Andreas Chiocchetti | 2023-02-20 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
| html | 0e20f22 | Andreas Chiocchetti | 2023-02-16 | Build site. |
| Rmd | eaa373d | Andreas Chiocchetti | 2023-02-16 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
| html | eaa373d | Andreas Chiocchetti | 2023-02-16 | workflowr::wflow_publish(c("./docs/", "./analysis/", "./code/*", |
Preprocessing
QC metrics from AG Hefendehl (Stroke)
Combined datasets and filter cells that have unique feature counts (gene number) over 7000 or less than 200 and cells with >5% mitochondrial counts.
# Visualize QC metrics as a violin plot
Idents(plates_hefendehl) <- "Plate"
VlnPlot(plates_hefendehl, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rp"),
group.by = "Plate", ncol = 4)
SampleMeta=plates_hefendehl@meta.data
cormt<- cor(SampleMeta$nCount_RNA, SampleMeta$percent.mt, method="spearman")
corfeat<- cor(SampleMeta$nCount_RNA, SampleMeta$nFeature_RNA, method="spearman")
plot1 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=percent.mt, col=Plate))+
geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 5)+labs(title=cormt)
plot2 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=nFeature_RNA, col=Plate))+geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 7000)+
ggtitle(corfeat)
plot1 | plot2
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
Perform integrated analysis
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| Version | Author | Date |
|---|---|---|
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
PCA, tSNE and UMAP from the first 30 dimensions and with a resolution of 0.8
# Visualization
Idents(samples.integrated) <- "seurat_clusters"
DimPlot(samples.integrated, reduction = "umap", split.by = "Treatment")
p1<- DimPlot(samples.integrated, reduction = "umap", group.by = "Plate")
p1a<- DimPlot(samples.integrated, reduction = "umap", group.by = "Mouse_ID")
p1b<- DimPlot(samples.integrated, reduction = "umap", group.by = "Genotype_Treatment")
p1 + p1a + p1b
p1b<- DimPlot(samples.integrated, reduction = "umap", group.by = "Genotype_Treatment",
pt.size = 1)
p1b
ggsave(file="./docs/UMAP_genotype_treatment.svg", p1b)Saving 6 x 6 in imageggsave(file="./docs/UMAP_genotype_treatment.pdf", p1b)Saving 6 x 6 in imagep1b<- DimPlot(samples.integrated[,which(grepl("Micro",samples.integrated@meta.data$Celltype))], reduction = "umap", group.by = "Genotype_Treatment", pt.size=1)
p1b
ggsave(file="./docs/UMAP_only_Macroglia_genotype_treatment.svg", p1b)Saving 6 x 6 in imageggsave(file="./docs/UMAP_only_Macroglia_genotype_treatment.pdf", p1b)Saving 6 x 6 in imageDimPlot(samples.integrated, group.by = "seurat_clusters", pt.size =1, label = T) + NoLegend()
DimPlot(samples.integrated, reduction = "umap", split.by = "Plate", pt.size= 1)
DimPlot(samples.integrated, reduction = "umap", split.by = "Genotype_Treatment", pt.size =1)
Cell cycle scoring
First, a score is assigned to each cell (Tirosh et al. 2016), based on its expression of G2/M and S phase markers. These markers should be anticorrelated in their expression levels and cells expression neither are likely not cycling and in G1 phase. Note: For downstream cell cylce regression the quantitative scores for G2/M and S phase are used, not the dicrete classification.
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
Cluster distribution
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Age",label = F) +
ggtitle("Cluster distribution according to Age") + theme_classic()| DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Genotype_Treatment",label = F) +
ggtitle("Cluster distribution according to Genotype + Treatment")+ theme_classic()
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Plate",label = F) +
ggtitle("Cluster distribution according to Plate")+ theme_classic() | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by = "seurat_clusters", label = T) +
ggtitle("Clustering")+ theme_classic()
Idents(samples.integrated) <- "seurat_clusters"
# use same colors for clusters as in plots
require(scales)
samples.integrated$seurat_clusters = factor(samples.integrated$seurat_clusters, levels=sort(as.character(unique(samples.integrated$seurat_clusters))))
identities <- levels(samples.integrated$seurat_clusters) # Create vector with levels of object@ident
cluster_colors <- hue_pal()(length(identities)) # Create vector of default ggplot2 colors
# number of cells in each cluster
cluster_nCell <- as.data.frame.matrix(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
cluster_nCell["Total" ,] = colSums(cluster_nCell)
cluster_nCell APPPS1_Ctrl APPPS1_Stroke WT_Ctrl WT_Stroke
0 164 38 108 62
1 28 15 28 15
2 10 5 42 0
3 0 0 4 0
4 0 0 8 0
5 6 1 5 3
6 1 4 7 8
Total 209 63 202 88# % of cells in each cluster , grouped by genotype_Age
cluster_per_Cell <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Age))
colnames(cluster_per_Cell) <- c("Cluster", "Age", "Frequency")
cluster_per_Cell Cluster Age Frequency
1 0 37 weeks 226
2 1 37 weeks 56
3 2 37 weeks 41
4 3 37 weeks 2
5 4 37 weeks 6
6 5 37 weeks 7
7 6 37 weeks 11
8 0 38 weeks 44
9 1 38 weeks 8
10 2 38 weeks 13
11 3 38 weeks 2
12 4 38 weeks 2
13 5 38 weeks 4
14 6 38 weeks 3
15 0 40 weeks 102
16 1 40 weeks 22
17 2 40 weeks 3
18 3 40 weeks 0
19 4 40 weeks 0
20 5 40 weeks 4
21 6 40 weeks 6cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Age), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Cluster", "Age", "Frequency")
cluster_percent_Cell Cluster Age Frequency
1 0 37 weeks 64.76
2 1 37 weeks 16.05
3 2 37 weeks 11.75
4 3 37 weeks 0.57
5 4 37 weeks 1.72
6 5 37 weeks 2.01
7 6 37 weeks 3.15
8 0 38 weeks 57.89
9 1 38 weeks 10.53
10 2 38 weeks 17.11
11 3 38 weeks 2.63
12 4 38 weeks 2.63
13 5 38 weeks 5.26
14 6 38 weeks 3.95
15 0 40 weeks 74.45
16 1 40 weeks 16.06
17 2 40 weeks 2.19
18 3 40 weeks 0.00
19 4 40 weeks 0.00
20 5 40 weeks 2.92
21 6 40 weeks 4.38cluster_per_Treatment <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
colnames(cluster_per_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_per_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 164
2 1 APPPS1_Ctrl 28
3 2 APPPS1_Ctrl 10
4 3 APPPS1_Ctrl 0
5 4 APPPS1_Ctrl 0
6 5 APPPS1_Ctrl 6
7 6 APPPS1_Ctrl 1
8 0 APPPS1_Stroke 38
9 1 APPPS1_Stroke 15
10 2 APPPS1_Stroke 5
11 3 APPPS1_Stroke 0
12 4 APPPS1_Stroke 0
13 5 APPPS1_Stroke 1
14 6 APPPS1_Stroke 4
15 0 WT_Ctrl 108
16 1 WT_Ctrl 28
17 2 WT_Ctrl 42
18 3 WT_Ctrl 4
19 4 WT_Ctrl 8
20 5 WT_Ctrl 5
21 6 WT_Ctrl 7
22 0 WT_Stroke 62
23 1 WT_Stroke 15
24 2 WT_Stroke 0
25 3 WT_Stroke 0
26 4 WT_Stroke 0
27 5 WT_Stroke 3
28 6 WT_Stroke 8cluster_percent_Treatment <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_percent_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 78.47
2 1 APPPS1_Ctrl 13.40
3 2 APPPS1_Ctrl 4.78
4 3 APPPS1_Ctrl 0.00
5 4 APPPS1_Ctrl 0.00
6 5 APPPS1_Ctrl 2.87
7 6 APPPS1_Ctrl 0.48
8 0 APPPS1_Stroke 60.32
9 1 APPPS1_Stroke 23.81
10 2 APPPS1_Stroke 7.94
11 3 APPPS1_Stroke 0.00
12 4 APPPS1_Stroke 0.00
13 5 APPPS1_Stroke 1.59
14 6 APPPS1_Stroke 6.35
15 0 WT_Ctrl 53.47
16 1 WT_Ctrl 13.86
17 2 WT_Ctrl 20.79
18 3 WT_Ctrl 1.98
19 4 WT_Ctrl 3.96
20 5 WT_Ctrl 2.48
21 6 WT_Ctrl 3.47
22 0 WT_Stroke 70.45
23 1 WT_Stroke 17.05
24 2 WT_Stroke 0.00
25 3 WT_Stroke 0.00
26 4 WT_Stroke 0.00
27 5 WT_Stroke 3.41
28 6 WT_Stroke 9.09# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Cell, aes(fill=Age,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="stack", stat="identity")+theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
a1 | b1 | c1
# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Treatment, aes(fill=Genotype_Treatment,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="stack", stat="identity")+theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=1))
a1 | b1 | c1
Cell type identification
Finding differentially expressed features (cluster biomarkers)
Idents(samples.integrated) <- samples.integrated$seurat_clusters
samples.integrated=PrepSCTFindMarkers(samples.integrated)
markers <- FindAllMarkers(object = samples.integrated,
only.pos = TRUE,
logfc.threshold = 0.25)
DefaultAssay(samples.integrated) = "SCT"
markers %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC) -> top20
DoHeatmap(samples.integrated, features = top20$gene) + NoLegend() + ggtitle("Top20 cluster marker genes")
SingleR
SingleR is an automatic annotation method for (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set. Here we use the built-in references “Immgen” (830 microarray samples of sorted hematopoetic and immune cell populations) and “Mouse RNA-Seq” (358 non-specific mouse RNA-seq samples).
Immgen reference
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
MouseRNA-Seq reference Section Skipped as pred.mouseRNA object is not available
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$MouseRNASeq_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "MouseRNASeq_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$MouseRNASeq_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("MouseRNASeq_sc_labels", "Genotype_Treatment", "Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to MouseRNASeq reference (absolut values)")+ theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to MouseRNASeq reference [%]")+ theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$Immgen_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$Immgen_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to Immgen reference (absolut values)") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Immgen reference [%]") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
xlab("")
Renaming Clusters
Idents(samples.integrated) = "Celltype"
DimPlot(samples.integrated, reduction = "umap", label=T) + NoLegend()+ theme_classic()
p1b<- DimPlot(samples.integrated, group.by = "Celltype", reduction="umap",
pt.size =1, label = T)
p1b
ggsave(file="./docs/UMAP_Cluster.svg", p1b)Saving 6 x 6 in imageggsave(file="./docs/UMAP_Cluster.pdf", p1b)Saving 6 x 6 in imagep1b<- DimPlot(samples.integrated[,which(grepl("Micro",samples.integrated@meta.data$Celltype))],
reduction = "umap", group.by = "Celltype",pt.size =1, label = T)
p1b
ggsave(file="./docs/UMAP_only_Macroglia_genotype_treatment.svg", p1b)Saving 6 x 6 in imageggsave(file="./docs/UMAP_only_Macroglia_genotype_treatment.pdf", p1b)Saving 6 x 6 in imageZoom into Microglia clusters
A second Seurat cluster is generated, where all non-microglial immune cells are removed according to the mouseRNASeq_sc_labels.
samples.microglia <- samples.integrated[,grepl("Microgli.*", samples.integrated$MouseRNASeq_sc_labels)]
p3 <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "MouseRNASeq_sc_labels") + theme_classic()+ NoLegend()
p3a <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "seurat_clusters") + theme_classic()+ NoLegend()
p3 + p3a
DAMs (Cell. 2017 Jun 15;169(7):1276-1290.e17. doi: 10.1016/j.cell.2017.05.018)
DAM plotting
DAM2_marker_gene_list <- list(Stage2_DAM_up)
samples.microglia <- AddModuleScore(object = samples.microglia,
features = DAM2_marker_gene_list, name = "DAM2_score")
p5 <- FeaturePlot(object = samples.microglia, features = "DAM2_score1")+scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.p5a <- DimPlot(object = samples.microglia, reduction = "umap")
DAM2cutoff = 2
phist = ggplot(data = samples.microglia@meta.data, aes(x=DAM2_score1))+
geom_density()+geom_vline(xintercept = DAM2cutoff)+
theme_classic()
samples.microglia$DAM2_binary = as.factor(samples.microglia$DAM2_score1>=DAM2cutoff)
pbin <- DimPlot(object = samples.microglia, group.by = "DAM2_binary")
phist | p5 | pbin| p5a 
DAM 2 Markers
DAM2 upreagulated markers
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
DAM2 dowreagulated markers
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
## points of interest spatial sequencing
Special Seq plotting
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
**comparing DAM vs Spatial
it seems as if the cells identified with the spatial are in fact DAM cells (if a cell is a DAM1 cell there is a 110 fold-higher probability of also being a Stroke spatial or Control Spatial cell than not)
There are just 8 cells in total that are not DAM cells but Stroke/Control spatial cells
microgila_only=which(grepl("Microglia", samples.microglia@meta.data$Celltype))
a = table(DAM1=samples.microglia@meta.data$DAM1_binary[microgila_only], Stroke_spatial=samples.microglia@meta.data$Stroke_spatial_binary[microgila_only])
b = table(DAM2=samples.microglia@meta.data$DAM2_binary[microgila_only], Stroke_spatial=samples.microglia@meta.data$Stroke_spatial_binary[microgila_only])
c = table(DAM1=samples.microglia@meta.data$DAM1_binary[microgila_only], Control_spatial=samples.microglia@meta.data$Control_spatial_binary[microgila_only])
d= table(DAM2=samples.microglia@meta.data$DAM2_binary[microgila_only], Control_spatial=samples.microglia@meta.data$Control_spatial_binary[microgila_only])
e= table(Control_spatial=samples.microglia@meta.data$Control_spatial_binary[microgila_only], Stroke_spatial=samples.microglia@meta.data$Stroke_spatial_binary[microgila_only])
a Stroke_spatial
DAM1 FALSE TRUE
FALSE 345 20
TRUE 22 150fisher.test(a)
Fisher's Exact Test for Count Data
data: a
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
59.72689 234.67294
sample estimates:
odds ratio
114.9418 b Stroke_spatial
DAM2 FALSE TRUE
FALSE 309 8
TRUE 58 162fisher.test(b)
Fisher's Exact Test for Count Data
data: b
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
49.27683 263.51616
sample estimates:
odds ratio
106.5147 c Control_spatial
DAM1 FALSE TRUE
FALSE 359 6
TRUE 134 38fisher.test(c)
Fisher's Exact Test for Count Data
data: c
p-value = 8.461e-15
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
6.866547 49.997490
sample estimates:
odds ratio
16.86419 d Control_spatial
DAM2 FALSE TRUE
FALSE 311 6
TRUE 182 38fisher.test(d)
Fisher's Exact Test for Count Data
data: d
p-value = 1.429e-10
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
4.40541 31.82585
sample estimates:
odds ratio
10.78139 e Stroke_spatial
Control_spatial FALSE TRUE
FALSE 359 134
TRUE 8 36fisher.test(e)
Fisher's Exact Test for Count Data
data: e
p-value = 9.942e-13
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
5.309353 30.642655
sample estimates:
odds ratio
11.99238 Renaming clusters
Idents(samples.microglia) <- "Celltype"
DimPlot(samples.microglia, reduction = "umap", label = TRUE, pt.size = 1)+ NoLegend()
Signature enrichment
All enrichment tests were done with gprofiler 2
Tested Ontologies and Signatures GO:MF = Molecular Function GO:BP = Biological Processes GO:CC = Cellular Compartment), KEGG = pathways from KEGG Reactome, TF = regulatory motif matches from TRANSFAC HPA = tissue specificity from Human Protein Atlas; CORUM = protein complexes from CORUM HP = human disease phenotypes from Human Phenotype Ontology.
For more dteails see Website: https://biit.cs.ut.ee/gprofiler/gost”
DEGs and GSEA across microglia of different genotypes
log fold-change of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. the APPPS1+ group)
WT vs MX04+
log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the Methoxy positive group
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
MX04+ vs MX04- in APPPS1 cohort only
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|== | 2%
|
|=== | 5%
|
|===== | 7%
|
|======= | 9%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 91%
|
|================================================================= | 93%
|
|=================================================================== | 95%
|
|==================================================================== | 98%
|
|======================================================================| 100%
|
| | 0%
|
|== | 2%
|
|=== | 5%
|
|===== | 7%
|
|======= | 9%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 91%
|
|================================================================= | 93%
|
|=================================================================== | 95%
|
|==================================================================== | 98%
|
|======================================================================| 100%
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
Stroke+ vs Stroke- in Control cohort only
positive FC indicate the gene is higher expressed in Stroke
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|================ | 22%
|
|================= | 24%
|
|=================== | 27%
|
|==================== | 29%
|
|===================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 39%
|
|============================= | 41%
|
|============================== | 43%
|
|=============================== | 45%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 55%
|
|======================================== | 57%
|
|========================================= | 59%
|
|=========================================== | 61%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|===================================================== | 76%
|
|====================================================== | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|================ | 22%
|
|================= | 24%
|
|=================== | 27%
|
|==================== | 29%
|
|===================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 39%
|
|============================= | 41%
|
|============================== | 43%
|
|=============================== | 45%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 55%
|
|======================================== | 57%
|
|========================================= | 59%
|
|=========================================== | 61%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|===================================================== | 76%
|
|====================================================== | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
| Version | Author | Date |
|---|---|---|
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
Stroke+ vs Stroke- in APPPS1 cohort only
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|== | 2%
|
|=== | 5%
|
|===== | 7%
|
|======= | 9%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 91%
|
|================================================================= | 93%
|
|=================================================================== | 95%
|
|==================================================================== | 98%
|
|======================================================================| 100%
|
| | 0%
|
|== | 2%
|
|=== | 5%
|
|===== | 7%
|
|======= | 9%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 91%
|
|================================================================= | 93%
|
|=================================================================== | 95%
|
|==================================================================== | 98%
|
|======================================================================| 100%
**FC vs FC plots
overlapgenes=union(rownames(markers_Stroke_APP), rownames(markers_Stroke_CTRL))
dataset=data.frame(APP_log2FC = markers_Stroke_APP[overlapgenes,]$avg_log2FC,
WT_log2FC = markers_Stroke_CTRL[overlapgenes,]$avg_log2FC)
isAPP<-abs(dataset$APP_log2FC)>0.58
isWT<-abs(dataset$WT_log2FC)>0.58
dataset$marker = "no-pref"
dataset$marker[which(isAPP)] = "APPS1+"
dataset$marker[which(isWT)] = "WT"
dataset$marker[which(isWT & isAPP)] = ifelse(abs(dataset$APP_log2FC[which(isWT & isAPP)])>
abs(dataset$WT_log2FC[which(isWT & isAPP)]),
"APPS1+", "WT")
dataset$genes = overlapgenes
dataset$topgene = F
top5APP = order(abs(dataset$APP_log2FC), decreasing = T)[1:5]
top5WT = order(abs(dataset$WT_log2FC), decreasing = T)[1:5]
dataset$topgene[union(top5APP, top5WT)]=T
crossFC_plot = ggplot(data=dataset,
aes(x=APP_log2FC, y=WT_log2FC, color=marker))+
geom_point()+theme_classic()+
geom_abline(slope = 1, lty=2)+
geom_abline(slope = -1, lty=2)+
geom_text(data=subset(dataset, topgene),
aes(label=genes), show.legend = FALSE, nudge_y = -0.5)
crossFC_plotWarning: Removed 3085 rows containing missing values (`geom_point()`).
ggsave(filename = "./output/CrossFCplot.svg", plot=crossFC_plot)Saving 6 x 6 in imageWarning: Removed 3085 rows containing missing values (`geom_point()`).ggsave(filename = "./output/CrossFCplot.pdf", plot=crossFC_plot)Saving 6 x 6 in imageWarning: Removed 3085 rows containing missing values (`geom_point()`).Stroke+ vs Stroke- in Stroke_binary_cells only
positive FC indicate the gene is higher expressed in Stroke
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|== | 3%
|
|==== | 5%
|
|===== | 8%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 15%
|
|============= | 18%
|
|============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================= | 41%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 59%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 79%
|
|========================================================= | 82%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 95%
|
|==================================================================== | 97%
|
|======================================================================| 100%
|
| | 0%
|
|== | 3%
|
|==== | 5%
|
|===== | 8%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 15%
|
|============= | 18%
|
|============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================= | 41%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 59%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 79%
|
|========================================================= | 82%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 95%
|
|==================================================================== | 97%
|
|======================================================================| 100%
DEGs and GSEA of microglia of different genotype and treatment
WT_Ctrl vs WT_Stroke
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|================ | 22%
|
|================= | 24%
|
|=================== | 27%
|
|==================== | 29%
|
|===================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 39%
|
|============================= | 41%
|
|============================== | 43%
|
|=============================== | 45%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 55%
|
|======================================== | 57%
|
|========================================= | 59%
|
|=========================================== | 61%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|===================================================== | 76%
|
|====================================================== | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|================ | 22%
|
|================= | 24%
|
|=================== | 27%
|
|==================== | 29%
|
|===================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 39%
|
|============================= | 41%
|
|============================== | 43%
|
|=============================== | 45%
|
|================================= | 47%
|
|================================== | 49%
|
|==================================== | 51%
|
|===================================== | 53%
|
|======================================= | 55%
|
|======================================== | 57%
|
|========================================= | 59%
|
|=========================================== | 61%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================= | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|===================================================== | 76%
|
|====================================================== | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
APP_Ctrl vs APP_Stroke
WT_Ctrl vs APP_Ctrl
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|===== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========= | 13%
|
|=========== | 15%
|
|============ | 17%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 25%
|
|=================== | 27%
|
|==================== | 29%
|
|====================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 38%
|
|============================ | 40%
|
|============================== | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|======================================== | 58%
|
|========================================== | 60%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|==================================================== | 75%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|========================================================== | 83%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|============================================================== | 88%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|===== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========= | 13%
|
|=========== | 15%
|
|============ | 17%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 25%
|
|=================== | 27%
|
|==================== | 29%
|
|====================== | 31%
|
|======================= | 33%
|
|======================== | 35%
|
|========================== | 37%
|
|=========================== | 38%
|
|============================ | 40%
|
|============================== | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|======================================== | 58%
|
|========================================== | 60%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|==================================================== | 75%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|========================================================== | 83%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|============================================================== | 88%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
WT_Stroke vs APP_Stroke
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
|
| | 0%
|
|== | 3%
|
|==== | 6%
|
|====== | 9%
|
|======== | 11%
|
|========== | 14%
|
|============ | 17%
|
|============== | 20%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|====================== | 31%
|
|======================== | 34%
|
|========================== | 37%
|
|============================ | 40%
|
|============================== | 43%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================== | 57%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 66%
|
|================================================ | 69%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 80%
|
|========================================================== | 83%
|
|============================================================ | 86%
|
|============================================================== | 89%
|
|================================================================ | 91%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%
|
| | 0%
|
|== | 3%
|
|==== | 6%
|
|====== | 9%
|
|======== | 11%
|
|========== | 14%
|
|============ | 17%
|
|============== | 20%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|====================== | 31%
|
|======================== | 34%
|
|========================== | 37%
|
|============================ | 40%
|
|============================== | 43%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================== | 57%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 66%
|
|================================================ | 69%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 80%
|
|========================================================== | 83%
|
|============================================================ | 86%
|
|============================================================== | 89%
|
|================================================================ | 91%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%
| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
DEGs and GSEA of each cluster
log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. Microglia 0) versus all others.
Genotype + Treatment comparison in each cluster
Idents(samples.microglia) <- "Genotype_Treatment"
mg1 <- samples.microglia[,samples.microglia$Celltype == "Microglia_1"]
mg1 <- SCTransform(mg1)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 23747 by 371Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 371 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 3 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 23747 genes
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 15%
|
|============ | 17%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 25%
|
|=================== | 27%
|
|==================== | 29%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 35%
|
|========================== | 38%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 62%
|
|============================================= | 65%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|==================================================== | 75%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|========================================================== | 83%
|
|============================================================ | 85%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%Computing corrected count matrix for 23747 genes
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|========= | 12%
|
|========== | 15%
|
|============ | 17%
|
|============= | 19%
|
|=============== | 21%
|
|================ | 23%
|
|================== | 25%
|
|=================== | 27%
|
|==================== | 29%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 35%
|
|========================== | 38%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|============================================ | 62%
|
|============================================= | 65%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 71%
|
|=================================================== | 73%
|
|==================================================== | 75%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|========================================================= | 81%
|
|========================================================== | 83%
|
|============================================================ | 85%
|
|============================================================= | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 6.962585 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg1.markers.Genotype_Treatment <- FindAllMarkers(mg1)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg1.markers.Genotype_Treatment)!=0){
mg1_top20 = mg1.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg1_top20chk=T
} else {mg1_top20chk=F}
mg2 <- samples.microglia[,samples.microglia$Celltype == "Microglia_2"]
mg2 <- SCTransform(mg2)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 15372 by 86Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 86 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 34 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 15372 genes
|
| | 0%
|
|== | 3%
|
|===== | 6%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 16%
|
|============== | 19%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|======================= | 32%
|
|========================= | 35%
|
|=========================== | 39%
|
|============================= | 42%
|
|================================ | 45%
|
|================================== | 48%
|
|==================================== | 52%
|
|====================================== | 55%
|
|========================================= | 58%
|
|=========================================== | 61%
|
|============================================= | 65%
|
|=============================================== | 68%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 81%
|
|=========================================================== | 84%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%Computing corrected count matrix for 15372 genes
|
| | 0%
|
|== | 3%
|
|===== | 6%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 16%
|
|============== | 19%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|======================= | 32%
|
|========================= | 35%
|
|=========================== | 39%
|
|============================= | 42%
|
|================================ | 45%
|
|================================== | 48%
|
|==================================== | 52%
|
|====================================== | 55%
|
|========================================= | 58%
|
|=========================================== | 61%
|
|============================================= | 65%
|
|=============================================== | 68%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 81%
|
|=========================================================== | 84%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 2.881491 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg2.markers.Genotype_Treatment <- FindAllMarkers(mg2)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg2.markers.Genotype_Treatment)!=0){
mg2_top20 = mg2.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg2_top20chk=T
} else {mg2_top20chk=F}
mg3 <- samples.microglia[,samples.microglia$Celltype == "Microglia_3"]
mg3 <- SCTransform(mg3)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 17416 by 57Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 57 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 4 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 36 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 17416 genes
|
| | 0%
|
|== | 3%
|
|==== | 6%
|
|====== | 9%
|
|======== | 11%
|
|========== | 14%
|
|============ | 17%
|
|============== | 20%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|====================== | 31%
|
|======================== | 34%
|
|========================== | 37%
|
|============================ | 40%
|
|============================== | 43%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================== | 57%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 66%
|
|================================================ | 69%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 80%
|
|========================================================== | 83%
|
|============================================================ | 86%
|
|============================================================== | 89%
|
|================================================================ | 91%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%Computing corrected count matrix for 17416 genes
|
| | 0%
|
|== | 3%
|
|==== | 6%
|
|====== | 9%
|
|======== | 11%
|
|========== | 14%
|
|============ | 17%
|
|============== | 20%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|====================== | 31%
|
|======================== | 34%
|
|========================== | 37%
|
|============================ | 40%
|
|============================== | 43%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================== | 57%
|
|========================================== | 60%
|
|============================================ | 63%
|
|============================================== | 66%
|
|================================================ | 69%
|
|================================================== | 71%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 80%
|
|========================================================== | 83%
|
|============================================================ | 86%
|
|============================================================== | 89%
|
|================================================================ | 91%
|
|================================================================== | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 2.862536 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg3.markers.Genotype_Treatment <- FindAllMarkers(mg3)Calculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(mg3.markers.Genotype_Treatment)!=0){
mg3_top20 = mg3.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg3_top20chk=T
} else {mg3_top20chk=F}
mg4 <- samples.microglia[,samples.microglia$Celltype == "Microglia_4"]
mg4 <- SCTransform(mg4)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 2188 by 8Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 8 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 25 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 47 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 2188 genes
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100%Computing corrected count matrix for 2188 genes
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.328267 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg4.markers.Genotype_Treatment <- FindAllMarkers(mg4)Calculating cluster WT_CtrlWarning: No DE genes identifiedWarning: The following tests were not performed:Warning: When testing WT_Ctrl versus all:
Cell group 2 is empty - no cells with identity class if(length(mg4.markers.Genotype_Treatment)!=0){
mg4_top20 = mg4.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg4_top20chk=T
} else {mg4_top20chk=F}
mg5 <- samples.microglia[,samples.microglia$Celltype == "Microglia_5"]
mg5 <- SCTransform(mg5)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 5172 by 15Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 15 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%Second step: Get residuals using fitted parameters for 5172 genes
|
| | 0%
|
|====== | 9%
|
|============= | 18%
|
|=================== | 27%
|
|========================= | 36%
|
|================================ | 45%
|
|====================================== | 55%
|
|============================================= | 64%
|
|=================================================== | 73%
|
|========================================================= | 82%
|
|================================================================ | 91%
|
|======================================================================| 100%Computing corrected count matrix for 5172 genes
|
| | 0%
|
|====== | 9%
|
|============= | 18%
|
|=================== | 27%
|
|========================= | 36%
|
|================================ | 45%
|
|====================================== | 55%
|
|============================================= | 64%
|
|=================================================== | 73%
|
|========================================================= | 82%
|
|================================================================ | 91%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 1.555766 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTmg5.markers.Genotype_Treatment <- FindAllMarkers(mg5)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_CtrlWarning: The following tests were not performed:Warning: When testing APPPS1_Stroke versus all:
Cell group 1 has fewer than 3 cellsif(length(mg5.markers.Genotype_Treatment)!=0){
mg5_top20 = mg5.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg5_top20chk=T
} else {mg5_top20chk=F}
sp_ctrl <- samples.microglia[,samples.microglia$Control_spatial_binary==T]
sp_ctrl <- SCTransform(sp_ctrl)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 11668 by 44Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 44 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 2 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 11668 genes
|
| | 0%
|
|=== | 4%
|
|====== | 8%
|
|========= | 12%
|
|============ | 17%
|
|=============== | 21%
|
|================== | 25%
|
|==================== | 29%
|
|======================= | 33%
|
|========================== | 38%
|
|============================= | 42%
|
|================================ | 46%
|
|=================================== | 50%
|
|====================================== | 54%
|
|========================================= | 58%
|
|============================================ | 62%
|
|=============================================== | 67%
|
|================================================== | 71%
|
|==================================================== | 75%
|
|======================================================= | 79%
|
|========================================================== | 83%
|
|============================================================= | 88%
|
|================================================================ | 92%
|
|=================================================================== | 96%
|
|======================================================================| 100%Computing corrected count matrix for 11668 genes
|
| | 0%
|
|=== | 4%
|
|====== | 8%
|
|========= | 12%
|
|============ | 17%
|
|=============== | 21%
|
|================== | 25%
|
|==================== | 29%
|
|======================= | 33%
|
|========================== | 38%
|
|============================= | 42%
|
|================================ | 46%
|
|=================================== | 50%
|
|====================================== | 54%
|
|========================================= | 58%
|
|============================================ | 62%
|
|=============================================== | 67%
|
|================================================== | 71%
|
|==================================================== | 75%
|
|======================================================= | 79%
|
|========================================================== | 83%
|
|============================================================= | 88%
|
|================================================================ | 92%
|
|=================================================================== | 96%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 2.196464 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTsp_ctrl.markers.Genotype_Treatment <- FindAllMarkers(sp_ctrl)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_Ctrlif(length(sp_ctrl.markers.Genotype_Treatment)!=0){
sp_ctrl_top20 = sp_ctrl.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
sp_ctrl_top20chk=T
} else {sp_ctrl_top20chk=F}
sp_stroke <- samples.microglia[,samples.microglia$Stroke_spatial_binary==T]
sp_stroke <- SCTransform(sp_stroke)Calculating cell attributes from input UMI matrix: log_umiVariance stabilizing transformation of count matrix of size 19085 by 170Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 170 cells
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07Found 3 outliers - those will be ignored in fitting/regularization stepSecond step: Get residuals using fitted parameters for 19085 genes
|
| | 0%
|
|== | 3%
|
|==== | 5%
|
|===== | 8%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 15%
|
|============= | 18%
|
|============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================= | 41%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 59%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 79%
|
|========================================================= | 82%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 95%
|
|==================================================================== | 97%
|
|======================================================================| 100%Computing corrected count matrix for 19085 genes
|
| | 0%
|
|== | 3%
|
|==== | 5%
|
|===== | 8%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 15%
|
|============= | 18%
|
|============== | 21%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 28%
|
|====================== | 31%
|
|======================= | 33%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================= | 41%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 49%
|
|==================================== | 51%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 59%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|=============================================== | 67%
|
|================================================ | 69%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|====================================================== | 77%
|
|======================================================== | 79%
|
|========================================================= | 82%
|
|=========================================================== | 85%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 92%
|
|================================================================== | 95%
|
|==================================================================== | 97%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 3.882366 secsDetermine variable featuresPlace corrected count matrix in counts slotCentering data matrixSet default assay to SCTsp_stroke.markers.Genotype_Treatment <- FindAllMarkers(sp_stroke)Calculating cluster WT_StrokeCalculating cluster APPPS1_CtrlCalculating cluster APPPS1_StrokeCalculating cluster WT_CtrlWarning: The following tests were not performed:Warning: When testing WT_Ctrl versus all:
Cell group 1 has fewer than 3 cellsif(length(sp_stroke.markers.Genotype_Treatment)!=0){
sp_stroke_top20 = sp_stroke.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
sp_stroke_top20chk=T
} else {sp_stroke_top20chk=F}Microglia_1
Microglia_2
Microglia_3
Microglia_4
[1] "no significant markers identified"Microglia_5
spatial Controls
spatial Stroke
save.image("./output/integrated_analysis.RData")
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DESeq2_1.38.3 scales_1.2.1
[3] gprofiler2_0.2.1 forcats_1.0.0
[5] stringr_1.5.0 dplyr_1.1.0
[7] purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.1.8
[11] tidyverse_1.3.2 pheatmap_1.0.12
[13] EnhancedVolcano_1.16.0 ggrepel_0.9.3
[15] SingleR_2.0.0 SummarizedExperiment_1.28.0
[17] Biobase_2.58.0 GenomicRanges_1.50.2
[19] GenomeInfoDb_1.34.9 IRanges_2.32.0
[21] S4Vectors_0.36.1 BiocGenerics_0.44.0
[23] MatrixGenerics_1.10.0 matrixStats_0.63.0
[25] ggplot2_3.4.1 SeuratObject_4.1.3
[27] Seurat_4.3.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.8 ragg_1.2.5
[3] bit64_4.0.5 knitr_1.42
[5] irlba_2.3.5.1 DelayedArray_0.24.0
[7] data.table_1.14.6 KEGGREST_1.38.0
[9] RCurl_1.98-1.10 generics_0.1.3
[11] ScaledMatrix_1.6.0 callr_3.7.3
[13] cowplot_1.1.1 RSQLite_2.2.20
[15] RANN_2.6.1 future_1.31.0
[17] bit_4.0.5 tzdb_0.3.0
[19] spatstat.data_3.0-0 xml2_1.3.3
[21] lubridate_1.9.2 httpuv_1.6.9
[23] assertthat_0.2.1 gargle_1.3.0
[25] xfun_0.37 hms_1.1.2
[27] jquerylib_0.1.4 evaluate_0.20
[29] promises_1.2.0.1 fansi_1.0.4
[31] dbplyr_2.3.0 readxl_1.4.2
[33] igraph_1.4.0 DBI_1.1.3
[35] geneplotter_1.76.0 htmlwidgets_1.6.1
[37] spatstat.geom_3.0-6 googledrive_2.0.0
[39] ellipsis_0.3.2 crosstalk_1.2.0
[41] backports_1.4.1 annotate_1.76.0
[43] deldir_1.0-6 sparseMatrixStats_1.10.0
[45] vctrs_0.5.2 ROCR_1.0-11
[47] abind_1.4-5 cachem_1.0.6
[49] withr_2.5.0 progressr_0.13.0
[51] sctransform_0.3.5 goftest_1.2-3
[53] svglite_2.1.1 cluster_2.1.4
[55] lazyeval_0.2.2 crayon_1.5.2
[57] spatstat.explore_3.0-6 pkgconfig_2.0.3
[59] labeling_0.4.2 nlme_3.1-162
[61] vipor_0.4.5 rlang_1.0.6
[63] globals_0.16.2 lifecycle_1.0.3
[65] miniUI_0.1.1.1 modelr_0.1.10
[67] rsvd_1.0.5 ggrastr_1.0.1
[69] cellranger_1.1.0 rprojroot_2.0.3
[71] polyclip_1.10-4 lmtest_0.9-40
[73] Matrix_1.5-3 zoo_1.8-11
[75] reprex_2.0.2 beeswarm_0.4.0
[77] whisker_0.4.1 ggridges_0.5.4
[79] processx_3.8.0 googlesheets4_1.0.1
[81] png_0.1-8 viridisLite_0.4.1
[83] bitops_1.0-7 getPass_0.2-2
[85] KernSmooth_2.23-20 Biostrings_2.66.0
[87] blob_1.2.3 DelayedMatrixStats_1.20.0
[89] parallelly_1.34.0 spatstat.random_3.1-3
[91] beachmat_2.14.0 memoise_2.0.1
[93] magrittr_2.0.3 plyr_1.8.8
[95] ica_1.0-3 zlibbioc_1.44.0
[97] compiler_4.2.2 RColorBrewer_1.1-3
[99] fitdistrplus_1.1-8 cli_3.6.0
[101] XVector_0.38.0 listenv_0.9.0
[103] patchwork_1.1.2 pbapply_1.7-0
[105] ps_1.7.2 MASS_7.3-58.2
[107] tidyselect_1.2.0 stringi_1.7.12
[109] textshaping_0.3.6 highr_0.10
[111] yaml_2.3.7 locfit_1.5-9.7
[113] BiocSingular_1.14.0 grid_4.2.2
[115] sass_0.4.5 tools_4.2.2
[117] timechange_0.2.0 future.apply_1.10.0
[119] parallel_4.2.2 rstudioapi_0.14
[121] git2r_0.31.0 gridExtra_2.3
[123] farver_2.1.1 Rtsne_0.16
[125] digest_0.6.31 shiny_1.7.4
[127] Rcpp_1.0.10 broom_1.0.3
[129] later_1.3.0 RcppAnnoy_0.0.20
[131] httr_1.4.4 AnnotationDbi_1.60.0
[133] colorspace_2.1-0 rvest_1.0.3
[135] XML_3.99-0.13 fs_1.6.1
[137] tensor_1.5 reticulate_1.28
[139] splines_4.2.2 uwot_0.1.14
[141] spatstat.utils_3.0-1 sp_1.6-0
[143] plotly_4.10.1 systemfonts_1.0.4
[145] xtable_1.8-4 jsonlite_1.8.4
[147] R6_2.5.1 pillar_1.8.1
[149] htmltools_0.5.4 mime_0.12
[151] DT_0.27 glue_1.6.2
[153] fastmap_1.1.0 BiocParallel_1.32.5
[155] codetools_0.2-19 utf8_1.2.3
[157] lattice_0.20-45 bslib_0.4.2
[159] spatstat.sparse_3.0-0 ggbeeswarm_0.7.1
[161] leiden_0.4.3 zip_2.2.2
[163] openxlsx_4.2.5.2 survival_3.5-3
[165] limma_3.54.1 rmarkdown_2.20
[167] munsell_0.5.0 GenomeInfoDbData_1.2.9
[169] haven_2.5.1 reshape2_1.4.4
[171] gtable_0.3.1